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摘要翻译：
本文讨论了在存在未观测暴露/结果变量的情况下，当变量之间的因果关系可用有向无环图和相应的联合分布递归分解来描述时，用观测数据评估因果效应的问题。首先，当一个未观察到的暴露/结果变量被认为包含两个以上类别时，我们提出了因果影响的可识别性标准。其次，当未观测结果变量与其代理变量之间存在未测量变量时，我们基于潜在结果方法给出了最紧的边界。本文的结果有助于在许多实际领域中难以或昂贵地观察一个暴露/结果变量的情况下评估因果效应。
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英文标题：
《The Evaluation of Causal Effects in Studies with an Unobserved
Exposure/Outcome Variable: Bounds and Identification》
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作者：
Manabu Kuroki, Zhihong Cai
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最新提交年份：
2012
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分类信息：
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
This paper deals with the problem of evaluating the causal effect using observational data in the presence of an unobserved exposure/ outcome variable, when cause-effect relationships between variables can be described as a directed acyclic graph and the corresponding recursive factorization of a joint distribution. First, we propose identifiability criteria for causal effects when an unobserved exposure/outcome variable is considered to contain more than two categories. Next, when unmeasured variables exist between an unobserved outcome variable and its proxy variables, we provide the tightest bounds based on the potential outcome approach. The results of this paper are helpful to evaluate causal effects in the case where it is difficult or expensive to observe an exposure/ outcome variable in many practical fields.
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PDF链接：
https://arxiv.org/pdf/1206.3267
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